In the context of Industry 4.0, Condition Based Maintenance (CBM) for complex systems is essential in order to identify failures and mitigate them. After the identification of a sensor set that guarantees the system monitoring, three main problems must be addressed for effective CBM: i) collection of the right data; ii) choice of the optimal technique to identify the specific data-set; iii) correct classification of the results. The solutions currently used are typically data driven and, therefore, the results are variable, as it is sometimes challenging to identify a pattern for all specific failures. This paper presents a solution that combines a data driven approach with an in-depth knowledge of the mechanical system’s behaviour. The choice of the right sensor set is calculated with the aid of the software MADe (Maintenance Aware Design environment), whereas the optimal data-set identification technique is pursued with a second tool called Syndrome Diagnostics. After an overview of such methodology, this work also presents RSGWPT (Redundant Second Generation Wavelet Packaged Transform) analysis to show different possible outcomes depending on the available sensor data and to tailor a detection technique to a given data set. Supervised and unsupervised learning techniques are tested to obtain either an anomaly detection or a failure identification depending on the chosen sensor set. By using the described method, it is possible to identify potential failures in the system with sufficient notice to implement the optimal maintenance actions.
Failure identification, Condition Based Maintenance, RSGWPT, Unsupervised learning, Supervised learning, Industry 4.0., Smart Factory
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